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Numerical approximation of RBSDEs via regularization

Author

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  • Ankush Agarwal

    (Western University, London, Canada)

  • Emmanuel Gobet

    (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité)

  • Yihan Zou

    (University of Glasgow)

Abstract

In this paper, we design a numerical scheme based on a regularization approach to approximate the solution of Reflected Backward Stochastic Differential Equation (RBSDE) and we study its convergence.We establish the order 1 convergence between the continuous regularized solution and the reflected solution, in full generality, as a function of the regularization parameter. The convergence between the continuous regularized solution and the corresponding RBSDE is obtained in both the almost sure and the L p (F)-sense (p ≥ 2). Additionally, we derive the convergence rate for the discretized version of the regularized RBSDE under mild regularity conditions.

Suggested Citation

  • Ankush Agarwal & Emmanuel Gobet & Yihan Zou, 2026. "Numerical approximation of RBSDEs via regularization," Working Papers hal-05671000, HAL.
    • Handle: RePEc:hal:wpaper:hal-05671000
    Note: View the original document on HAL open archive server: https://hal.science/hal-05671000v1
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